Newark City 4311 data anaysis
# Required libs
library(httr)
library(jsonlite)
library(lubridate)
library(tidyverse)
library(stringr)
library(leaflet)
#install.packages("DT")
library(rpart)
library(DT)
library(forecast)
library(rpart.plot)
library(caret)
Parsing Newark Historic data
urls.hist = c("http://data.ci.newark.nj.us/mk/api/3/action/datastore_search?resource_id=1cebcf44-3b2c-4f60-9d5d-817cb35bf9df&limit=40000",
"http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%221cebcf44-3b2c-4f60-9d5d-817cb35bf9df%22%20WHERE%20_id%20%3E%2031889",
"http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%221cebcf44-3b2c-4f60-9d5d-817cb35bf9df%22%20WHERE%20_id%20%3E%2063889",
"http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%221cebcf44-3b2c-4f60-9d5d-817cb35bf9df%22%20WHERE%20_id%20%3E%2095888")
xhist = data.frame()
for(i in urls.hist ){
raw.result <- GET(url = i)
this.raw.content <- rawToChar(raw.result$content)
this.content <- fromJSON(this.raw.content)
json_file <- lapply(this.content, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
xhistoric = this.content$result$records
xhistoric = xhistoric[,c("Type Name","Type Id","Street Address" , "Longitude","Request Id","Request Date", "Latitude" , "_id" ,"ID")]
xhist = rbind(xhist,xhistoric)
}
dim(xhist)
[1] 107614 9
Parsing 2015 Data
url = "http://data.ci.newark.nj.us/mk/api/3/action/datastore_search?resource_id=bc8e29eb-3c53-418c-a42a-4dbbaae668c2&limit=60000"
raw.result <- GET(url = url)
this.raw.content <- rawToChar(raw.result$content)
this.content <- fromJSON(this.raw.content)
json_file <- lapply(this.content, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
x2015 = this.content$result$records
dim(x2015)
[1] 14618 10
Parsing 2018 Data
urls.2018 = c("http://data.ci.newark.nj.us/mk/api/3/action/datastore_search?resource_id=b923c1ad-7246-400b-a593-785f67677b94&limit=68408",
"http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%22b923c1ad-7246-400b-a593-785f67677b94%22%20WHERE%20_id%20%3E%2031993",
"http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%22b923c1ad-7246-400b-a593-785f67677b94%22%20WHERE%20_id%20%3E%2066495")
x2018 = data.frame()
for(i in urls.2018 ){
raw.result <- GET(url = i)
this.raw.content <- rawToChar(raw.result$content)
this.content <- fromJSON(this.raw.content)
json_file <- lapply(this.content, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
xtemp = this.content$result$records
xtemp = xtemp[,c("Complaint","DateCreated","Typename","Location","Lot", "ComplaintID","Department" ,"_id" ,"Block")]
x2018 = rbind(x2018,xtemp)
}
dim(x2018)
[1] 71685 9
names(xhist)
[1] "Type Name" "Type Id" "Street Address" "Longitude" "Request Id" "Request Date" "Latitude"
[8] "_id" "ID"
names(x2015)
[1] "Type Name" "Type Id" "Street Name" "Longitude" "Request Id" "Request Date" "Street Number" "Latitude"
[9] "_id" "ID"
names(x2018)
[1] "Complaint" "DateCreated" "Typename" "Location" "Lot" "ComplaintID" "Department" "_id" "Block"
Fixing the 2015 dataset
x2015$`Street Address` = paste(x2015$`Street Number`,x2015$`Street Name`)
x2015$`Street Name` = NULL
x2015$`Street Number` = NULL
sum(is.na(x2018))
[1] 4075
sum(complete.cases(x2018))
[1] 70259
Assign Blank cells with NA’s for x2015
x2015$`Street Address`[x2015$`Street Address` == " "] = NA
sum(is.na(x2015))
[1] 2070
Assign Blank cells with NA’sfor x2018
xhist[xhist == " "] = NA
sum(is.na(xhist))
[1] 3862
Parse Date
xhist$`Request Date` = as.POSIXct(xhist$`Request Date`)
x2015$`Request Date` = "2015"
x2015$`Request Date` = as.Date(x2015$`Request Date`,format = "%Y")
x2018$DateCreated = as.POSIXct(x2018$DateCreated, format = "%b %d %Y")
Creating new combined dataset for x2015 and xhist
till.2015 = rbind(xhist,x2015)
names(till.2015)[1:3]<-c("Complaint","ComplaintID","Location")
names(till.2015)[6]<-c("DateCreated")
overall4311 Dataset with common attributes
overall4311 = rbind(till.2015[,c(1,3,6)], x2018[,c(1,4,2)])
fillColor = "#FFA07A"
fillColor2 = "#F1C40F"
x2018 %>%
group_by(Typename) %>%
summarise(Count = n()) %>%
ungroup() %>%
mutate(complaint_description = reorder(Typename,Count)) %>%
arrange(desc(Count)) %>%
head(10) %>%
ggplot(aes(x = reorder(Typename,Count),y = Count )) +
geom_bar(stat='identity',colour="white", fill = fillColor2) +
geom_text(aes(x = complaint_description, y = 1, label = paste0("(",Count,")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Complaint Descriptors',
y = 'Count',
title = 'Highest Complaint Typename 2018') +
coord_flip() +
theme_bw()

Combing the datasets for getting overall Complaint typename
temp = c(xhist$`Type Name`,x2015$`Type Name`,x2018$Typename)
m1 <- matrix(temp, ncol=1, byrow=TRUE)
overall.complaints <- as.data.frame(m1, stringsAsFactors=FALSE)
overall.complaints
dim(overall.complaints)
[1] 193917 1
overall.complaints %>%
group_by(V1) %>%
summarise(Count = n()) %>%
ungroup() %>%
mutate(complaint_description = reorder(V1,Count)) %>%
arrange(desc(Count)) %>%
head(20) %>%
ggplot(aes(x = reorder(V1,Count),y = Count )) +
geom_bar(stat='identity',colour="white", fill = fillColor2) +
geom_text(aes(x = complaint_description, y = 1, label = paste0("(",Count,")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Complaint Descriptors',
y = 'Count',
title = 'Highest Complaint Typename') +
coord_flip() +
theme_bw()

Cluster Analysis of Complaints through Location
till.2015$Longitude = as.numeric(till.2015$Longitude)
till.2015$Latitude = as.numeric(till.2015$Latitude)
center_lon = median(till.2015$Longitude,na.rm = TRUE)
View(m1)
center_lat = median(till.2015$Latitude,na.rm = TRUE)
till.2015 %>% leaflet() %>%addProviderTiles("Esri.NatGeoWorldMap") %>%
addMarkers(lng = ~Longitude, lat = ~Latitude,clusterOptions = markerClusterOptions()) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 12)
names(xhist)[6] = "DateCreated"
Trend of 4311 calls
xhist %>%
mutate(year = format(DateCreated, format="%Y") )%>%
mutate(month = format(DateCreated, format="%m")) %>%
filter(!is.na(year)) %>%
filter(!is.na(month)) %>%
group_by(year,month) %>%
summarise(Count = n()) %>%
arrange(year,month) %>%
mutate(YearMonth = make_date(year=year,month=month) ) %>%
ggplot(aes(x=YearMonth,y=Count)) +
geom_line(size=1, color="red")+
geom_point(size=3, color="red") +
labs(x = 'Time', y = 'Count',title = 'Trend of 4311 Calls') +
theme_bw()

Newark4311TrendData = xhist %>%
mutate(year = format(DateCreated, format="%Y") ) %>%
mutate(month = format(DateCreated, format="%m")) %>%
filter(!is.na(year)) %>%
filter(!is.na(month)) %>%
group_by(year,month) %>%
summarise(Count = n()) %>%
arrange(year,month)
tsNewark4311TrendData = ts(Newark4311TrendData)
datatable((tsNewark4311TrendData), style="bootstrap", class="table-condensed", options = list(dom = 'tp',scrollX = TRUE))
fit <- auto.arima(tsNewark4311TrendData[,3])
preds = forecast(fit, h = 5)
preds %>% autoplot(include=40) +theme_bw()

till.2015 %>%
mutate(month = format(DateCreated, format="%m")) %>%
filter(!is.na(month)) %>%
group_by(month) %>%
summarise(Count = n()) %>%
arrange(desc(Count)) %>%
ungroup() %>%
mutate(month = reorder(month,Count)) %>%
ggplot(aes(x = month,y = Count)) +
geom_bar(stat='identity',colour="white", fill = fillColor2) +
geom_text(aes(x = month, y = 1, label = paste0("(",Count,")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Month',
y = 'Count',
title = 'Months with service requests counts') +
coord_flip() +
theme_bw()

x2018 %>%
mutate(year = format(DateCreated, format="%Y") )%>%
mutate(month = format(DateCreated, format="%m")) %>%
filter(!is.na(year)) %>%
filter(!is.na(month)) %>%
group_by(year,month) %>%
summarise(Count = n()) %>%
arrange(year,month) %>%
mutate(YearMonth = make_date(year=year,month=month) ) %>%
ggplot(aes(x=YearMonth,y=Count)) +
geom_line(size=1, color="red")+
geom_point(size=3, color="red") +
labs(x = 'Time', y = 'Count',title = 'Trend of 4311 Calls') +
theme_bw()
View(till.2015)

x2018 %>%
mutate(month = format(DateCreated, format="%m")) %>%
filter(!is.na(month)) %>%
group_by(month) %>%
summarise(Count = n()) %>%
arrange(desc(Count)) %>%
ungroup() %>%
mutate(month = reorder(month,Count)) %>%
ggplot(aes(x = month,y = Count)) +
geom_bar(stat='identity',colour="white", fill = fillColor2) +
geom_text(aes(x = month, y = 1, label = paste0("(",Count,")",sep="")),
hjust=0, vjust=.5, size = 4, colour = 'black',
fontface = 'bold') +
labs(x = 'Month',
y = 'Count',
title = 'Months with service requests counts') +
coord_flip() +
theme_bw()

na.omit(till.2015) %>%
group_by(Location) %>%
summarise(Count = length(Location)) %>%
arrange(desc(Count), .by_group = TRUE)
NA
Location Analisis of 2018
na.omit(x2018) %>%
group_by(Location) %>%
summarise(Count = length(Location)) %>%
arrange(desc(Count), .by_group = TRUE)
NA
Departmants in 2018
x2018 %>%
group_by(Typename,Department) %>%
summarise(Count = n()) %>%
ungroup() %>%
mutate(complaint_description = reorder(Department,Count)) %>%
arrange(desc(Count), .by_group = TRUE) %>%
head(50) %>%
ggplot(aes(x = reorder(Department,Count),y = Count)) +
geom_bar(stat='identity',colour="white", fill = fillColor) +
labs(x = 'Agencies',
y = 'Complaint Count',
title = 'Number of Comlpaints With Agency') +
coord_flip() +
theme_bw()

na.omit(x2018) %>%
group_by(Location,Typename) %>%
summarise(Count = length(Location)) %>%
arrange(desc(Count), .by_group = TRUE)
NA
na.omit(x2018) %>%
group_by(Location,Typename) %>%
summarise(Count = length(Typename)) %>%
arrange(desc(Count), .by_group = TRUE)
Newark4311SampleAll = till.2015 %>%
filter(!is.na(Latitude) ) %>%
filter(!is.na(Longitude))
leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircles(data = Newark4311SampleAll,lng = ~Longitude, lat = ~Latitude,
color = ~c("red")) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 15)
library(leaflet.extras)
leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
setView(lng=center_lon, lat=center_lat,zoom = 17) %>%
addHeatmap( data = Newark4311SampleAll,
lng = ~Longitude, lat = ~Latitude,
blur = 20, max = 0.05, radius = 15
) %>%
addResetMapButton() %>%
addSearchGoogle() %>%
addFullscreenControl()
top10complaints = na.omit(till.2015) %>%
group_by(Complaint) %>%
summarise(Count = length(Location)) %>%
arrange(desc(Count), .by_group = TRUE)
#as.factor(till.2015$Complaint)
grepl(paste(except, collapse = "|"),ab)
[1] TRUE
write.csv(til.2015, file = "till2015.csv",row.names=FALSE)
Error in is.data.frame(x) : object 'til.2015' not found
names(till.2015)
[1] "Complaint" "ComplaintID" "Location" "Longitude" "Request Id" "DateCreated" "Latitude" "_id" "ID"
x2018$Latitude = geocoded$lat
x2018$Longitude = geocoded$long
Geocoded2018 %>%
filter(!is.na(lat) ) %>%
filter(!is.na(long)) %>%
leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addMarkers(lng = ~long, lat = ~lat
,clusterOptions = markerClusterOptions()) %>%
# controls
setView(lng=center_lon, lat=center_lat,zoom = 15)
---
title: "Newark City 4311"
author: "Akhil Patil"
output: html_notebook
---

Newark City 4311 data anaysis

```{r}
# Required libs
library(httr)
library(jsonlite)
library(lubridate)

library(tidyverse)
library(stringr)
library(leaflet)
#install.packages("DT")
library(rpart)
library(DT)
library(forecast)
library(rpart.plot)
library(caret)

```



Parsing Newark Historic data
```{r}

urls.hist = c("http://data.ci.newark.nj.us/mk/api/3/action/datastore_search?resource_id=1cebcf44-3b2c-4f60-9d5d-817cb35bf9df&limit=40000",
              "http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%221cebcf44-3b2c-4f60-9d5d-817cb35bf9df%22%20WHERE%20_id%20%3E%2031889",
              "http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%221cebcf44-3b2c-4f60-9d5d-817cb35bf9df%22%20WHERE%20_id%20%3E%2063889",
              "http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%221cebcf44-3b2c-4f60-9d5d-817cb35bf9df%22%20WHERE%20_id%20%3E%2095888")
xhist = data.frame()

for(i in urls.hist ){
  raw.result <- GET(url = i)
  this.raw.content <- rawToChar(raw.result$content)
  this.content <- fromJSON(this.raw.content)
  json_file <- lapply(this.content, function(x) {
    x[sapply(x, is.null)] <- NA
    unlist(x)
  })
  
  xhistoric = this.content$result$records
  xhistoric = xhistoric[,c("Type Name","Type Id","Street Address" , "Longitude","Request Id","Request Date", "Latitude" , "_id" ,"ID")]
  xhist = rbind(xhist,xhistoric)
}

dim(xhist)
```


Parsing 2015 Data
```{r}

url = "http://data.ci.newark.nj.us/mk/api/3/action/datastore_search?resource_id=bc8e29eb-3c53-418c-a42a-4dbbaae668c2&limit=60000"

raw.result <- GET(url = url)
this.raw.content <- rawToChar(raw.result$content)
this.content <- fromJSON(this.raw.content)

json_file <- lapply(this.content, function(x) {
  x[sapply(x, is.null)] <- NA
  unlist(x)
})

x2015 = this.content$result$records
dim(x2015)

```


Parsing 2018 Data
```{r}

urls.2018 = c("http://data.ci.newark.nj.us/mk/api/3/action/datastore_search?resource_id=b923c1ad-7246-400b-a593-785f67677b94&limit=68408",
              "http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%22b923c1ad-7246-400b-a593-785f67677b94%22%20WHERE%20_id%20%3E%2031993",
              "http://data.ci.newark.nj.us/mk/api/3/action/datastore_search_sql?sql=SELECT%20*%20from%20%22b923c1ad-7246-400b-a593-785f67677b94%22%20WHERE%20_id%20%3E%2066495")
x2018 = data.frame()

for(i in urls.2018 ){
  raw.result <- GET(url = i)
  this.raw.content <- rawToChar(raw.result$content)
  this.content <- fromJSON(this.raw.content)
  json_file <- lapply(this.content, function(x) {
    x[sapply(x, is.null)] <- NA
    unlist(x)
  })
  
  xtemp = this.content$result$records
  xtemp = xtemp[,c("Complaint","DateCreated","Typename","Location","Lot", "ComplaintID","Department" ,"_id" ,"Block")]
  x2018 = rbind(x2018,xtemp)
}

dim(x2018)
```

```{r}
names(xhist)
names(x2015)
names(x2018)
```


Fixing the 2015 dataset

```{r}
x2015$`Street Address` = paste(x2015$`Street Number`,x2015$`Street Name`)

x2015$`Street Name` = NULL
x2015$`Street Number` = NULL
```

```{r}
sum(is.na(x2018))
sum(complete.cases(x2018))
```

Assign Blank cells with NA's for x2015 
```{r}
x2015$`Street Address`[x2015$`Street Address` == " "] = NA
sum(is.na(x2015))
```

Assign Blank cells with NA'sfor x2018

```{r}
xhist[xhist == " "] = NA
sum(is.na(xhist))
```

Parse Date
```{r}

xhist$`Request Date` = as.POSIXct(xhist$`Request Date`)

x2015$`Request Date` = "2015"
x2015$`Request Date` = as.Date(x2015$`Request Date`,format = "%Y")


x2018$DateCreated = as.POSIXct(x2018$DateCreated, format = "%b %d %Y")

```

Creating new combined dataset for x2015 and xhist
```{r}
till.2015 = rbind(xhist,x2015)
```


```{r}
names(till.2015)[1:3]<-c("Complaint","ComplaintID","Location")
names(till.2015)[6]<-c("DateCreated")

```

overall4311  Dataset with common attributes

```{r}
overall4311 = rbind(till.2015[,c(1,3,6)], x2018[,c(1,4,2)])
```


```{r}
fillColor = "#FFA07A"
fillColor2 = "#F1C40F"

```

```{r}
x2018 %>%
  group_by(Typename) %>%
  summarise(Count = n()) %>%
  ungroup() %>%
  mutate(complaint_description = reorder(Typename,Count)) %>%
  arrange(desc(Count)) %>%
  head(10) %>%
  
  ggplot(aes(x = reorder(Typename,Count),y = Count )) +
  geom_bar(stat='identity',colour="white", fill = fillColor2) +
  geom_text(aes(x = complaint_description, y = 1, label = paste0("(",Count,")",sep="")),
            hjust=0, vjust=.5, size = 4, colour = 'black',
            fontface = 'bold') +
  labs(x = 'Complaint Descriptors', 
       y = 'Count', 
       title = 'Highest Complaint Typename 2018') +
  coord_flip() + 
  theme_bw()

```

Combing the datasets for getting overall Complaint typename
```{r}
temp = c(xhist$`Type Name`,x2015$`Type Name`,x2018$Typename)

m1 <- matrix(temp, ncol=1, byrow=TRUE)
overall.complaints <- as.data.frame(m1, stringsAsFactors=FALSE)
overall.complaints
dim(overall.complaints)
```
```{r}
overall.complaints %>%
  group_by(V1) %>%
  summarise(Count = n()) %>%
  ungroup() %>%
  mutate(complaint_description = reorder(V1,Count)) %>%
  arrange(desc(Count)) %>%
  head(20) %>%
  
  ggplot(aes(x = reorder(V1,Count),y = Count )) +
  geom_bar(stat='identity',colour="white", fill = fillColor2) +
  geom_text(aes(x = complaint_description, y = 1, label = paste0("(",Count,")",sep="")),
            hjust=0, vjust=.5, size = 4, colour = 'black',
            fontface = 'bold') +
  labs(x = 'Complaint Descriptors', 
       y = 'Count', 
       title = 'Highest Complaint Typename') +
  coord_flip() + 
  theme_bw()

```

Cluster Analysis of Complaints through Location

```{r}
till.2015$Longitude = as.numeric(till.2015$Longitude)
till.2015$Latitude =  as.numeric(till.2015$Latitude)
center_lon = median(till.2015$Longitude,na.rm = TRUE)
center_lat = median(till.2015$Latitude,na.rm = TRUE)
```


```{r}
till.2015 %>% leaflet() %>%addProviderTiles("Esri.NatGeoWorldMap") %>%
  
  addMarkers(lng = ~Longitude, lat = ~Latitude,clusterOptions = markerClusterOptions()) %>%
    # controls
  
  setView(lng=center_lon, lat=center_lat,zoom = 12) 
```

```{r}
names(xhist)[6] = "DateCreated"

```

Trend of 4311 calls
```{r}

xhist %>%
  mutate(year = format(DateCreated, format="%Y") )%>%
  mutate(month = format(DateCreated, format="%m")) %>%
  filter(!is.na(year)) %>%
  filter(!is.na(month)) %>%
  group_by(year,month) %>%
  summarise(Count = n()) %>%
  arrange(year,month) %>%
  mutate(YearMonth = make_date(year=year,month=month) ) %>%
  

  ggplot(aes(x=YearMonth,y=Count)) +
  geom_line(size=1, color="red")+
  geom_point(size=3, color="red") +
  labs(x = 'Time', y = 'Count',title = 'Trend of 4311 Calls') +
  theme_bw()
```

```{r}
Newark4311TrendData = xhist %>%
  mutate(year = format(DateCreated, format="%Y") ) %>%
  mutate(month = format(DateCreated, format="%m")) %>%
  filter(!is.na(year)) %>%
  filter(!is.na(month)) %>%
  group_by(year,month) %>%
  summarise(Count = n()) %>%
  arrange(year,month)

tsNewark4311TrendData = ts(Newark4311TrendData)

datatable((tsNewark4311TrendData), style="bootstrap", class="table-condensed", options = list(dom = 'tp',scrollX = TRUE))
```

```{r}
fit <- auto.arima(tsNewark4311TrendData[,3])

preds = forecast(fit, h = 5)

preds %>% autoplot(include=40) +theme_bw()
```



```{r}
till.2015 %>%
  mutate(month = format(DateCreated, format="%m")) %>%
  filter(!is.na(month)) %>%
  group_by(month) %>%
  summarise(Count = n()) %>%
  arrange(desc(Count)) %>%
  ungroup() %>%
  mutate(month = reorder(month,Count)) %>%
  
    ggplot(aes(x = month,y = Count)) +
    geom_bar(stat='identity',colour="white", fill = fillColor2) +
    geom_text(aes(x = month, y = 1, label = paste0("(",Count,")",sep="")),
              hjust=0, vjust=.5, size = 4, colour = 'black',
              fontface = 'bold') +
    labs(x = 'Month', 
         y = 'Count', 
         title = 'Months with service requests counts') +
    coord_flip() + 
    theme_bw()
```


```{r}

x2018 %>%
  mutate(year = format(DateCreated, format="%Y") )%>%
  mutate(month = format(DateCreated, format="%m")) %>%
  filter(!is.na(year)) %>%
  filter(!is.na(month)) %>%
  group_by(year,month) %>%
  summarise(Count = n()) %>%
  arrange(year,month) %>%
  mutate(YearMonth = make_date(year=year,month=month) ) %>%
  

  ggplot(aes(x=YearMonth,y=Count)) +
  geom_line(size=1, color="red")+
  geom_point(size=3, color="red") +
  labs(x = 'Time', y = 'Count',title = 'Trend of 4311 Calls') +
  theme_bw()
```

```{r}
x2018 %>%
  mutate(month = format(DateCreated, format="%m")) %>%
  filter(!is.na(month)) %>%
  group_by(month) %>%
  summarise(Count = n()) %>%
  arrange(desc(Count)) %>%
  ungroup() %>%
  mutate(month = reorder(month,Count)) %>%
  
    ggplot(aes(x = month,y = Count)) +
    geom_bar(stat='identity',colour="white", fill = fillColor2) +
    geom_text(aes(x = month, y = 1, label = paste0("(",Count,")",sep="")),
              hjust=0, vjust=.5, size = 4, colour = 'black',
              fontface = 'bold') +
    labs(x = 'Month', 
         y = 'Count', 
         title = 'Months with service requests counts') +
    coord_flip() + 
    theme_bw()
```

```{r}
na.omit(till.2015) %>%
  group_by(Location) %>%
  summarise(Count = length(Location)) %>%
  arrange(desc(Count), .by_group = TRUE)
  
```

Location Analisis of 2018

```{r}
na.omit(x2018) %>%
  group_by(Location) %>%
  summarise(Count = length(Location)) %>%
  arrange(desc(Count), .by_group = TRUE)
  
```

Departmants in 2018
```{r}
x2018 %>%
  group_by(Typename,Department) %>%
  summarise(Count = n()) %>%
  ungroup() %>%
  mutate(complaint_description = reorder(Department,Count)) %>%
  arrange(desc(Count), .by_group = TRUE) %>%
  head(50) %>%
  
  ggplot(aes(x = reorder(Department,Count),y = Count)) +
  geom_bar(stat='identity',colour="white", fill = fillColor) +
  labs(x = 'Agencies', 
       y = 'Complaint Count', 
       title = 'Number of Comlpaints With Agency') +
  coord_flip() + 
  theme_bw()

```

```{r}
na.omit(x2018) %>%
  group_by(Location,Typename) %>%
  summarise(Count = length(Location)) %>%
  arrange(desc(Count), .by_group = TRUE)
 
```

```{r}
na.omit(x2018) %>%
  group_by(Location,Typename)  %>%
  summarise(Count = length(Typename)) %>%
  arrange(desc(Count), .by_group = TRUE)

```

```{r}

Newark4311SampleAll = till.2015 %>%
  filter(!is.na(Latitude) ) %>%
  filter(!is.na(Longitude)) 

leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
  
addCircles(data = Newark4311SampleAll,lng = ~Longitude, lat = ~Latitude, 
           color = ~c("red"))  %>%

  # controls
  setView(lng=center_lon, lat=center_lat,zoom = 15) 

```

```{r}
library(leaflet.extras)
leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
  setView(lng=center_lon, lat=center_lat,zoom = 17) %>%
  addHeatmap( data = Newark4311SampleAll,
    lng = ~Longitude, lat = ~Latitude, 
    blur = 20, max = 0.05, radius = 15
) %>%
  addResetMapButton() %>%
  addSearchGoogle() %>%
  addFullscreenControl()


```

```{r}
top10complaints = na.omit(till.2015) %>%
  group_by(Complaint) %>%
  summarise(Count = length(Location)) %>%
  arrange(desc(Count), .by_group = TRUE)
  

```


```{r}
#as.factor(till.2015$Complaint)
```

```{r}
x2018$Location %>% 
   str_subset(pattern = "^*11TH AVE*")
ab = "169 ALEXANDER ST, Newark,New Jersey" 
if(str_subset(pattern = "^*ALEXANDER*")){
  print("OK")
}
except = c("ALEXANDER","11TH AVE")
grepl(paste(except, collapse = "|"),ab)
```

 
```{r}
# Geocoding script for large list of addresses. 

# Shane Lynn 10/10/2013
#install.packages("ggmap")

#load up the ggmap library
library(ggmap)
# get the input data
infile <- "input"
data <- x2018

# get the address list, and append "Ireland" to the end to increase accuracy 
# (change or remove this if your address already include a country etc.)
addresses = data$Location
addresses = paste0(addresses, ", Newark,New Jersey")

except = c("ALEXANDER","11TH AVE","NORWOOD","WEST END AVE","SALEM","STUYVESANT","CLINTON","PLEASANT")


#define a function that will process googles server responses for us.
getGeoDetails <- function(address){ 
  if(grepl(paste(except, collapse = "|"),address)){
    return(data.frame(lat=NA, long=NA, accuracy=NA, formatted_address=NA, address_type=NA, status=NA)
   )
  }
   #use the gecode function to query google servers
   geo_reply = geocode(address, output='all', messaging=TRUE, override_limit=TRUE)
   #now extract the bits that we need from the returned list
   answer <- data.frame(lat=NA, long=NA, accuracy=NA, formatted_address=NA, address_type=NA, status=NA)
   
   answer$status <- geo_reply$status

   #if we are over the query limit - want to pause for an hour
   while(geo_reply$status == "OVER_QUERY_LIMIT"){
       print("OVER QUERY LIMIT - Pausing for 1 hour at:") 
       time <- Sys.time()
       print(as.character(time))
       Sys.sleep(60*60)
       geo_reply = geocode(address, output='all', messaging=TRUE, override_limit=TRUE)
       answer$status <- geo_reply$status
   }

   #return Na's if we didn't get a match:
   if (geo_reply$status != "OK"){
       return(answer)
   }
   #else, extract what we need from the Google server reply into a dataframe:
   answer$lat <- geo_reply$results[[1]]$geometry$location$lat
   answer$long <- geo_reply$results[[1]]$geometry$location$lng   
   if (length(geo_reply$results[[1]]$types) > 0){
       answer$accuracy <- geo_reply$results[[1]]$types[[1]]
   }
   answer$address_type <- paste(geo_reply$results[[1]]$types, collapse=',')
   answer$formatted_address <- geo_reply$results[[1]]$formatted_address

   return(answer)
}

#initialise a dataframe to hold the results
geocoded <- data.frame()
# find out where to start in the address list (if the script was interrupted before):
startindex <- 1
#if a temp file exists - load it up and count the rows!
tempfilename <- paste0(infile, '_temp_geocoded.rds')
if (file.exists(tempfilename)){
       print("Found temp file - resuming from index:")
       geocoded <- readRDS(tempfilename)
       startindex <- nrow(geocoded)
       print(startindex)
}

# Start the geocoding process - address by address. geocode() function takes care of query speed limit.
for (ii in seq(startindex, length(addresses))){
   print(paste("Working on index", ii, "of", length(addresses)))
   #query the google geocoder - this will pause here if we are over the limit.
   result = getGeoDetails(addresses[ii]) 
   print(result$status)     
   result$index <- ii
   #append the answer to the results file.
   geocoded <- rbind(geocoded, result)
   #save temporary results as we are going along
   saveRDS(geocoded, tempfilename)
}

#now we add the latitude and longitude to the main data
data$lat <- geocoded$lat
data$long <- geocoded$long
data$accuracy <- geocoded$accuracy

#finally write it all to the output files
saveRDS(data, paste0("../data/", infile ,"_geocoded.rds"))
write.table(data, file=paste0("../data/", infile ,"_geocoded.csv"), sep=",", row.names=FALSE)

```

```{r}
dim(geocoded)

geocoded = geocoded[-c(527),]

write.csv(geocoded, file = "Geocoded2018.csv",row.names=FALSE)

write.csv(x2018, file = "x2018.csv",row.names=FALSE)


write.csv(till.2015, file = "till2015.csv",row.names=FALSE)


write.csv(x2015, file = "x2015.csv",row.names=FALSE)


write.csv(xhist, file = "xhist.csv",row.names=FALSE)


write.csv(overall4311, file = "overall4311.csv",row.names=FALSE)

```

```{r}
names(till.2015)
x2018$Latitude = geocoded$lat

x2018$Longitude = geocoded$long

```

```{r}
Geocoded2018 %>%
  filter(!is.na(lat) ) %>%
  filter(!is.na(long)) %>%

leaflet() %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
  
addMarkers(lng = ~long, lat = ~lat
           ,clusterOptions = markerClusterOptions())  %>%

  # controls
  setView(lng=center_lon, lat=center_lat,zoom = 15) 

```

```{r}

```

